Hyperspectral imaging: a novel approach for microscopic analysis

Compact Linnik-type hyperspectral quantitative phase microscope for advanced classification of cellular components

Hyperspectral quantitative phase microscopy (HS-QPM) involves the acquisition of phase images across narrow spectral bands, which enables wavelength-dependent study of different biological samples. In the present work, a compact Linnik-type HS-QPM system is developed to reduce the instability and complexity associated with conventional HS-QPM techniques. The use of a single objective lens for both reference and sample arms makes the setup compact. The capabilities of the system are demonstrated by evaluating the HS phase map of both industrial and biological specimens. Phase maps of exfoliated cheek cells at different wavelengths are stacked to form a HS phase cube, adding spectral dimensionality to spatial phase distribution. Analysis of wavelength response of different cellular components are performed using principal component analysis to identify dominant spectral features present in the HS phase dataset. Findings of the study emphasize on the efficiency and effectiveness of HS-QPM for advancing cellular characterization in biomedical research and clinical applications.

A Simple Doublet Lens Design for Mid-Infrared Imaging

Wide-field mid-infrared (MIR) hyperspectral imaging offers a promising approach for studying heterogeneous chemical systems due to its ability to independently characterize the molecular properties of different regions of a sample. However, applications of wide-field MIR microscopy are limited to spatial resolutions no better than ∼1 μm. While methods exist to overcome the classical diffraction limit of ∼λ/2, chromatic aberration from transmissive imaging reduces the achievable resolution. Here we describe the design and implementation of a simple MIR achromatic lens combination that we believe will aid in the development of resolution-enhanced wide-field MIR hyperspectral optical and chemical absorption imaging. We also examine the use of this doublet lens to image through polystyrene microspheres, an emerging and simple means for enhancing spatial resolution.

Hyperspectral Imaging Detects Clitoral Vascular Issues in Gender-Affirming Surgery

The aim of this study was to assess the efficacy of hyperspectral imaging (HSI) as an intraoperative perfusion imaging modality during gender affirmation surgery (GAS). The hypothesis posited that HSI could quantify perfusion to the clitoral complex, thereby enabling the prediction of either uneventful wound healing or the occurrence of necrosis. In this non-randomised prospective clinical study, we enrolled 30 patients who underwent GAS in the form of vaginoplasty with the preparation of a clitoral complex from 2020 to 2024 and compared patients' characteristics as well as HSI data regarding clitoris necrosis. Individuals demonstrating uneventful wound healing pertaining to the clitoral complex were designated as Group A. Patients with complete necrosis of the neo-clitoris were assigned to Group B. Patient characteristics were collected and subsequently a comparative analysis carried out. No significant difference in patient characteristics was observed between the two groups. Necrosis occurred when both StO2 and NIR PI parameters fell below 40%. For the simultaneous occurrence of StO2 and NIR PI of 40% or less, a sensitivity of 92% and specificity of 72% was calculated. Intraoperatively, the onset of necrosis in the clitoral complex can be reliably predicted with the assistance of HSI.

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection

Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.

From sensor fusion to knowledge distillation in collaborative LIBS and hyperspectral imaging for mineral identification

Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

Microsphere-assisted super-resolved hyperspectral microscopy

Hyperspectral microscopy (HSM) combines conventional microscopy with basic hyperspectral imaging and results in 3D microscopic spatio-spectral information. The combination comes along with a variety of applications, such as detection and classification of different material properties through spectral fingerprints, which otherwise cannot be detected with a color camera alone. However, similar to other microscopies, the resolution of HSM is limited by diffraction. In recent years microsphere (MS)-assisted microscopy has attracted intensive attention for resolution enhancement. Here, we extend the MS idea into HSM. We show that MS-HSM reveals extra hyperspectral information about the specimen which is lost without it. Moreover, the insertion of MS results in super-resolved images. The resolution enhancement and obtaining the extra hyperspectral information can be tuned by the MS size.

Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration

Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The diffraction-limited THz signals highly constrain the performances of existing restoration methods. To address the problem, we propose a novel multi-view Subspace-Attention-guided Restoration Network (SARNet) that fuses multi-view and multi-spectral features of THz images for effective image restoration and 3D tomographic reconstruction. To this end, SARNet uses multi-scale branches to extract intra-view spatio-spectral amplitude and phase features and fuse them via shared subspace projection and self-attention guidance. We then perform inter-view fusion to further improve the restoration of individual views by leveraging the redundancies between neighboring views. Here, we experimentally construct a THz time-domain spectroscopy (THz-TDS) system covering a broad frequency range from 0.1 to 4 THz for building up a temporal/spectral/spatial/material THz database of hidden 3D objects. Complementary to a quantitative evaluation, we demonstrate the effectiveness of our SARNet model on 3D THz tomographic reconstruction applications.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11263-023-01812-y.

Design and Validation of a Custom-Made Hyperspectral Microscope Imaging System for Biomedical Applications

Hyperspectral microscope imaging (HMI) is an emerging modality that integrates spatial information collected by standard laboratory microscopy and the spectral-based contrast obtained by hyperspectral imaging and may be instrumental in establishing novel quantitative diagnostic methodologies, particularly in histopathology. Further expansion of HMI capabilities hinges upon the modularity and versatility of systems and their proper standardization. In this report, we describe the design, calibration, characterization, and validation of the custom-made laboratory HMI system based on a Zeiss Axiotron fully motorized microscope and a custom-developed Czerny-Turner-type monochromator. For these important steps, we rely on a previously designed calibration protocol. Validation of the system demonstrates a performance comparable to classic spectrometry laboratory systems. We further demonstrate validation against a laboratory hyperspectral imaging system for macroscopic samples, enabling future comparison of spectral imaging results across length scales. An example of the utility of our custom-made HMI system on a standard hematoxylin and eosin-stained histology slide is also shown.

Multifaceted mirror array illuminator for fluorescence excitation-scanning spectral imaging microscopy

SIGNIFICANCE

Hyperspectral imaging (HSI) technologies offer great potential in fluorescence microscopy for multiplexed imaging, autofluorescence removal, and analysis of autofluorescent molecules. However, there are also associated trade-offs when implementing HSI in fluorescence microscopy systems, such as decreased acquisition speed, resolution, or field-of-view due to the need to acquire spectral information in addition to spatial information. The vast majority of HSI fluorescence microscopy systems provide spectral discrimination by filtering or dispersing the fluorescence emission, which may result in loss of emitted fluorescence signal due to optical filters, dispersive optics, or supporting optics, such as slits and collimators. Technologies that scan the fluorescence excitation spectrum may offer an approach to mitigate some of these trade-offs by decreasing the complexity of the emission light path.

AIM

We describe the development of an optical technique for hyperspectral imaging fluorescence excitation-scanning (HIFEX) on a microscope system.

APPROACH

The approach is based on the design of an array of wavelength-dependent light emitting diodes (LEDs) and a unique beam combining system that uses a multifurcated mirror. The system was modeled and optimized using optical ray trace simulations, and a prototype was built and coupled to an inverted microscope platform. The prototype system was calibrated, and initial feasibility testing was performed by imaging multilabel slide preparations.

RESULTS

We present results from optical ray trace simulations, prototyping, calibration, and feasibility testing of the system. Results indicate that the system can discriminate between at least six fluorescent labels and autofluorescence and that the approach can provide decreased wavelength switching times, in comparison with mechanically tuned filters.

CONCLUSIONS

We anticipate that LED-based HIFEX microscopy may provide improved performance for time-dependent and photosensitive assays.

Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function

This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial features within the hyperspectral cube are incorporated to train a strong classifier. Unlike 2D convolutions, 3D convolutions take the spectral dimension into account while automatically collecting distinctive features during the CNN training stage. As a result, we have avoided manual feature engineering on hyperspectral data and proposed a compact method for HSI medical applications. Moreover, the focal loss function, utilized as a CNN cost function, enables our model to tackle the class imbalance problem residing in the dataset effectively. The focal loss function emphasizes the hard examples to learn and prevents overfitting due to the lack of inter-class balancing. Our empirical results demonstrate the superiority of hyperspectral data over RGB data for liver cancer tissue classification. We have observed that increased spectral dimension results in higher classification accuracy. Both spectral and spatial features are essential in training an accurate learner for cancer tissue classification.

Rapid Identification of Infectious Pathogens at the Single-Cell Level via Combining Hyperspectral Microscopic Images and Deep Learning

Identifying infectious pathogens quickly and accurately is significant for patients and doctors. Identifying single bacterial strains is significant in eliminating culture and speeding up diagnosis. We present an advanced optical method for the rapid detection of infectious (including common and uncommon) pathogens by combining hyperspectral microscopic imaging and deep learning. To acquire more information regarding the pathogens, we developed a hyperspectral microscopic imaging system with a wide wavelength range and fine spectral resolution. Furthermore, an end-to-end deep learning network based on feature fusion, called BI-Net, was designed to extract the species-dependent features encoded in cell-level hyperspectral images as the fingerprints for species differentiation. After being trained based on a large-scale dataset that we built to identify common pathogens, BI-Net was used to classify uncommon pathogens via transfer learning. An extensive analysis demonstrated that BI-Net was able to learn species-dependent characteristics, with the classification accuracy and Kappa coefficients being 92% and 0.92, respectively, for both common and uncommon species. Our method outperformed state-of-the-art methods by a large margin and its excellent performance demonstrates its excellent potential in clinical practice.

Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning

Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyperspectral microscopic imaging (HMI) technology can be used as a powerful tool for skin cancer diagnosis by reflecting the changes in the physical structure and microenvironment of the sample through the differences in the HMI data cube. Based on spectral data, this work studied the staging identification of SCC and the influence of the selected region of interest (ROI) on the staging results. In the SCC staging identification process, the optimal result corresponded to the standard normal variate transformation (SNV) for spectra preprocessing, the partial least squares (PLS) for dimensionality reduction, the hold-out method for dataset partition and the random forest (RF) model for staging identification, with the highest staging accuracy of 0.952 ± 0.014, and a kappa value of 0.928 ± 0.022. By comparing the staging results based on spectral characteristics from the nuclear compartments and peripheral regions, the spectral data of the nuclear compartments were found to contribute more to the accurate staging of SCC.

Two-photon peak molecular brightness spectra reveal long-wavelength enhancements of multiplexed imaging depth and photostability

The broad use of two-photon microscopy has been enabled in part by Ti:Sapphire femtosecond lasers, which offer a wavelength-tunable source of pulsed excitation. Action spectra have thus been primarily reported for the tunable range of Ti:Sapphire lasers (∼700-1000 nm). However, longer wavelengths offer deeper imaging in tissue via reduced scattering and spectral dips in water absorption, and new generations of pulsed lasers offer wider tunable ranges. We present the peak molecular brightness spectra for eight Alexa Fluor dyes between 700-1300 nm as a first-order surrogate for action spectra measured with an unmodified commercial microscope, which reveal overlapping long-wavelength excitation peaks with potential for multiplexed excitation. We demonstrate simultaneous single-wavelength excitation of six spectrally overlapping fluorophores using either short (∼790 nm) or long (∼1090 nm) wavelengths, and that the newly characterized excitation peaks measured past 1000 nm offer improved photostability and enhanced fidelity of linear spectral unmixing at depth compared to shorter wavelengths.

Pigment analysis based on a line-scanning fluorescence hyperspectral imaging microscope combined with multivariate curve resolution

A rapid and cost-effective system is vital for the detection of harmful algae that causes environmental problems in terms of water quality. The approach for algae detection was to capture images based on hyperspectral fluorescence imaging microscope by detecting specific fluorescence signatures. With the high degree of overlapping spectra of algae, the distribution of pigment in the region of interest was unknown according to a previous report. We propose an optimization method of multivariate curve resolution (MCR) to improve the performance of pigment analysis. The reconstruction image described location and concentration of the microalgae pigments. This result indicated the cyanobacterial pigment distribution and mapped the relative pigment content. In conclusion, with the advantage of acquiring two-dimensional images across a range of spectra, HSI conjoining spectral features with spatial information efficiently estimated specific features of harmful microalgae in MCR models.

Compressive spectral image fusion via a single aperture high throughput imaging system

Spectral image fusion techniques combine the detailed spatial information of a multispectral (MS) image and the rich spectral information of a hyperspectral (HS) image into a high-spatial and high-spectral resolution image. Due to the data deluge entailed by such images, new imaging modalities have exploited their intrinsic correlations in such a way that, a computational algorithm can fuse them from few multiplexed linear projections. The latter has been coined compressive spectral image fusion. State-of-the-art research work have focused mainly on the algorithmic part, simulating instrumentation characteristics and assuming independently registered sensors to conduct compressed MS and HS imaging. In this manuscript, we report on the construction of a unified computational imaging framework that includes a proof-of-concept optical testbed to simultaneously acquire MS and HS compressed projections, and an alternating direction method of multipliers algorithm to reconstruct high-spatial and high-spectral resolution images from the fused compressed measurements. The testbed employs a digital micro-mirror device (DMD) to encode and split the input light towards two compressive imaging arms, which collect MS and HS measurements, respectively. This strategy entails full light throughput sensing since no light is thrown away by the coding process. Further, different resolutions can be dynamically tested by binning the DMD and sensors pixels. Real spectral responses and optical characteristics of the employed equipment are obtained through a per-pixel point spread function calibration approach to enable accurate compressed image fusion performance. The proposed framework is demonstrated through real experiments within the visible spectral range using as few as 5% of the data.

Experimental demonstration of spectral domain computational ghost imaging

We demonstrate computational spectral-domain ghost imaging by encoding complementary Fourier patterns directly onto the spectrum of a superluminescent laser diode using a programmable spectral filter. Spectral encoding before the object enables uniform spectral illumination across the beam profile, removing the need for light collection optics and yielding increased signal-to-noise ratio. In addition, the use of complementary Fourier patterns allows reduction of deleterious of parasitic light effects. As a proof-of-concept, we measure the wavelength-dependent transmission of a Michelson interferometer and a wavelength-division multiplexer. Our results open new perspectives for remote broadband spectral measurements.

Dark-field hyperspectral imaging for label free detection of nano-bio-materials

Nanomaterials are playing an increasingly important role in cancer diagnosis and treatment. Nanoparticle (NP)-based technologies have been utilized for targeted drug delivery during chemotherapies, photodynamic therapy, and immunotherapy. Another active area of research is the toxicity studies of these nanomaterials to understand the cellular uptake and transport of these materials in cells, tissues, and environment. Traditional techniques such as transmission electron microscopy, and mass spectrometry to analyze NP-based cellular transport or toxicity effect are expensive, require extensive sample preparation, and are low-throughput. Dark-field hyperspectral imaging (DF-HSI), an integration of spectroscopy and microscopy/imaging, provides the ability to investigate cellular transport of these NPs and to quantify the distribution of them within bio-materials. DF-HSI also offers versatility in non-invasively monitoring microorganisms, single cell, and proteins. DF-HSI is a low-cost, label-free technique that is minimally invasive and is a viable choice for obtaining high-throughput quantitative molecular analyses. Multimodal imaging modalities such as Fourier transform infrared and Raman spectroscopy are also being integrated with HSI systems to enable chemical imaging of the samples. HSI technology is being applied in surgeries to obtain molecular information about the tissues in real-time. This article provides brief overview of fundamental principles of DF-HSI and its application for nanomaterials, protein-detection, single-cell analysis, microbiology, surgical procedures along with technical challenges and future integrative approach with other imaging and measurement modalities. This article is categorized under: Diagnostic Tools > in vitro Nanoparticle-Based Sensing Diagnostic Tools > in vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery.

Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning

We propose a new method for the analysis and classification of HSI images. The method uses deep learning to interpret the molecular vibrational behaviour of healthy and tumoral human epithelial tissue, based on data gathered via SWIR (short-wave infrared) spectroscopy. We analyzed samples of Melanoma, Dysplastic Nevus and healthy skin. Preliminary results show that human epithelial tissue is sensitive to SWIR to the point of making possible the differentiation between healthy and tumor tissues. We conclude that HSI-SWIR can be used to build new methods for tumor classification.

Stokes shift microscopy by excitation and emission imaging

In this contribution, we present a new method, based on a tunable excitation laser source and a robust common path interferometer in the detection channel. Its purpose is to image spectral excitation and emission information on a monochrome complementary metal oxide semiconductor (CMOS) camera. This allows us to spatially obtain both excitation and emission spectra of the whole imaged area and create derived images such as red-green-blue (RGB), excitation and emission maxima, and Stokes shift images. Our presented method is a further development of hyperspectral imaging that usually is limited to recording spatially resolved emission spectra. Taking advantage of the full camera chip should speed up the acquisition versus line scan or pointwise hyperspectral imaging.

Integration of point cloud data and hyperspectral imaging as a data gathering methodology for refurbishment projects using building information modelling (BIM)

Purpose

Building information modelling (BIM) is a digital representation of the physical and functional characteristics of a building. Its use offers a range of benefits in terms of achieving the efficient design, construction, operation and maintenance of buildings. Applying BIM at the outset of a new build project should be relatively easy. However, it is often problematic to apply BIM techniques to an existing building, for example, as part of a refurbishment project or as a tool supporting the facilities management strategy, because of inadequacies in the previous management of the dataset that characterises the facility in question. These inadequacies may include information on as built geometry and materials of construction. By the application of automated retrospective data gathering for use in BIM, such problems should be largely overcome and significant benefits in terms of efficiency gains and cost savings should be achieved.

Design/methodology/approach

Laser scanning can be used to collect geometrical and spatial information in the form of a 3D point cloud, and this technique is already used. However, as a point cloud representation does not contain any semantic information or geometrical context, such point cloud data must refer to external sources of data, such as building specification and construction materials, to be in used in BIM.

Findings

Hyperspectral imaging techniques can be applied to provide both spectral and spatial information of scenes as a set of high-resolution images. Integrating of a 3D point cloud into hyperspectral images would enable accurate identification and classification of surface materials and would also convert the 3D representation to BIM.

Originality/value

This integrated approach has been applied in other areas, for example, in crop management. The transfer of this approach to facilities management and construction would improve the efficiency and automation of the data transition from building pathology to BIM. In this study, the technological feasibility and advantages of the integration of laser scanning and hyperspectral imaging (the latter not having previously been used in the construction context in its own right) is discussed, and an example of the use of a new integration technique is presented, applied for the first time in the context of buildings.

Spatial resolution enhancement for pushbroom-based microscopic hyperspectral imaging

Hyperspectral imaging (HSI) is a promising tool for microscopic histopathology studies. Pushbroom microscopic hyperspectral imaging systems are widely used because of their low cost and easy implementation. However, the spatial resolution of pushbroom HSI systems is limited by the width of the optical entrance slit. A narrower slit leads to longer exposure time and slower imaging speed. In this paper, we explored several spatial resolution enhancement algorithms, originally designed for remote-sensing hyperspectral imaging, for pushbroom microscopic HSI systems. Our results demonstrate that those algorithms could effectively achieve a higher spatial resolution without sacrificing imaging speed.

A Spherical Mirror-based Illumination System for Fluorescence Excitation-Scanning Hyperspectral Imaging

Many hardware approaches have been developed for implementing hyperspectral imaging on fluorescence microscope systems; each with tradeoffs in spectral sensitivity and spectral, spatial, and temporal sampling. For example, tunable filter-based systems typically have limited wavelength switching speeds and sensitivities that preclude high-speed spectral imaging. Here, we present a novel approach combining multiple illumination wavelengths using solid state LEDs in a 2-mirror configuration similar to a Cassegrain reflector assembly. This approach provides spectral discrimination by scanning a range of fluorescence excitation wavelengths, which we have previously shown can improve spectral image acquisition time compared to traditional fluorescence emission-scanning hyperspectral imaging. In this work, the geometry of the LED and other optical components was optimized. A model of the spectral illuminator was designed using TracePro ray tracing software (Lambda Research Corp.) that included an emitter, lens, Spherical mirror, flat mirror, and liquid light guide input. A parametric sensitivity study was performed to optimize the optical throughput varying the LED viewing angle, properties of the Spherical reflectors, the lens configuration, focal length, and position. The following factors significantly affected the optical throughput: LED viewing angle, lens position, and lens focal length. Several types of configurations were evaluated, and an optimized lens and LED position were determined. Initial optimization results indicate that a 10% optical transmission can be achieved for either a 16 or 32 wavelength system. Future work will include continuing to optimize the ray trace model, prototyping, and experimental testing of the optimized configuration.

Dual-type hyperspectral microscopic imaging for the identification and analysis of intestinal fungi

A method based on a dual-type (transmission and fluorescence) hyperspectral microscopic image system was developed to identify species of intestinal fungi. Living fungi are difficult to identify via transmission spectra or fluorescence spectra alone. We propose an identification method based on both fluorescence and transmission spectra that employs a series of image processing methods. Three species of intestinal fungi were used to evaluate the method. The results demonstrate that the specificity of the model trained with dual-type spectra was 98.36%, whereas the specificities achieved by training with fluorescence spectra and transmission spectra alone were 94.04% and 92.88%, respectively.

Characterization of essential oil distribution in the root cross-section of Valeriana officinalis L. s.l. by using histological imaging techniques

BACKGROUND

The essential oil is an important compound of the root and rhizome of medicinally used valerian (Valeriana officinalis L. s.l.), with a stated minimum content in the European pharmacopoeia. The essential oil is located in droplets, of which the position and distribution in the total root cross-section of different valerian varieties, root thicknesses and root horizons are determined in this study using an adapted fluorescence-microscopy and automatic imaging analysis method. The study was initiated by the following facts:A probable negative correlation between essential oil content and root thickness in selected single plants (elites), observed during the breeding of coarsely rooted valerian with high oil content.Higher essential oil content after careful hand-harvest and processing of the roots.

RESULTS

In preliminary tests, the existence of oil containing droplets in the outer and inner regions of the valerian roots was confirmed by histological techniques and light-microscopy, as well as Fourier-transform infrared spectroscopy. Based on this, fluorescence-microscopy followed by image analysis of entire root cross-sections, showed that a large number of oil droplets (on average 43% of total oil droplets) are located close to the root surface. The remaining oil droplets are located in the inner regions (parenchyma) and showed varying density gradients from the inner to the outer regions depending on genotype, root thickness and harvesting depth.

CONCLUSIONS

Fluorescence-microscopy is suitable to evaluate prevalence and distribution of essential oil droplets of valerian in entire root cross-sections. The oil droplet density gradient varies among genotypes. Genotypes with a linear rather than an exponential increase of oil droplet density from the inner to the outer parenchyma can be chosen for better stability during post-harvest processing. The negative correlation of essential oil content and root thickness as observed in our breeding material can be counteracted through a selection towards generally high oil droplet density levels, and large oil droplet sizes independent of root thickness.

Single-Cell Analysis Using Hyperspectral Imaging Modalities

Almost a decade ago, hyperspectral imaging (HSI) was employed by the NASA in satellite imaging applications such as remote sensing technology. This technology has since been extensively used in the exploration of minerals, agricultural purposes, water resources, and urban development needs. Due to recent advancements in optical re-construction and imaging, HSI can now be applied down to micro- and nanometer scales possibly allowing for exquisite control and analysis of single cell to complex biological systems. This short review provides a description of the working principle of the HSI technology and how HSI can be used to assist, substitute, and validate traditional imaging technologies. This is followed by a description of the use of HSI for biological analysis and medical diagnostics with emphasis on single-cell analysis using HSI.

Demystifying autofluorescence with excitation-scanning hyperspectral imaging

Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. Here, we propose to utilize excitation-scanning of autofluorescence to examine tissues and diagnose pathologies. Spectra of autofluorescent molecules were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Scans utilized excitation wavelengths from 360 nm to 550 nm in 5 nm increments. The resultant spectra were used to examine hyperspectral image stacks from various collaborative studies, including an atherosclerotic rat model and a colon cancer study. Hyperspectral images were analyzed with ENVI and custom Matlab scripts including linear spectral unmixing (LSU) and principal component analysis (PCA). Initial results suggest the ability to separate the signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states of similar tissues. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitation-scanning hyperspectral imaging. Future work will expand the library of pure molecules and will examine more defined disease states.

Applications and assessment of an excitation-scanning hyperspectral imaging system

The majority of microscopic and endoscopic technologies utilize white light illumination. For a number of applications, hyper-spectral imaging can be shown to have significant improvements over standard white-light imaging techniques. This is true for both microscopy and in vivo imaging. However, hyperspectral imaging methods have suffered from slow application times. Often, minutes are required to gather a full imaging stack. Here we will describe and evaluate a novel excitation-scanning hyperspectral imaging system and discuss some applications. We have developed and are optimizing a novel approach called excitation-scanning hyperspectral imaging that provides an order of magnitude increased signal strength. This excitation scanning technique has enabled us to produce a microscopy system capable of high speed hyperspectral imaging with the potential for live video acquisition. The excitation-scanning hyperspectral imaging technology we developed may impact a range of applications. The current design uses digital strobing to illuminate at 16 wavelengths with millisecond image acquisition time. Analog intensity control enables a fully customizable excitation profile. A significant advantage of excitation-scanning hyperspectral imaging is can identify multiple targets simultaneously in real time. Finally, we are exploring utilizing this technology for a variety of applications ranging from measuring cAMP distribution in three dimensions within a cell to electrophysiology.

A theoretical-experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists.

Characterizing the Nano-Bio Interface Using Microscopic Techniques: Imaging the Cell System is Just as Important as Imaging the Nanoparticle System

The rapid growth of nanotechnology and its industries has elevated the need to understand the risks associated with handling, using, and disposing of nanomaterials. These risks can be assessed through exposure measurement and hazard identification. One of the common challenges associated with quantifying nanomaterials in products, waste, humans, or the environment is the lack of tools available to measure concentration. The ability of refined tools and techniques to qualitatively detect nanoparticles in complex matrices has been demonstrated. For biological and ecological tests systems, dose can be represented as initial concentration in the applied matrix, concentration administered during the route of exposure, concentration at the target organ, and intake concentration at the cellular level. Each of these concentration measurements requires different sets of tools to perform accurate analyses. Advances in microscopy techniques provide new opportunities for reporting observations occurring at the interaction of a nanoparticle with a biomolecular entity of similar size within a biological test(s) system. This protocol outlines the steps to image nanomaterials within cell-based systems. © 2017 by John Wiley & Sons, Inc.

Lenslet array tunable snapshot imaging spectrometer (LATIS) for hyperspectral fluorescence microscopy

Snapshot hyperspectral imaging augments pixel dwell time and acquisition speeds over existing scanning systems, making it a powerful tool for fluorescence microscopy. While most snapshot systems contain fixed datacube parameters (x,y,λ), our novel snapshot system, called the lenslet array tunable snapshot imaging spectrometer (LATIS), demonstrates tuning its average spectral resolution from 22.66 nm (80x80x22) to 13.94 nm (88x88x46) over 485 to 660 nm. We also describe a fixed LATIS with a datacube of 200x200x27 for larger field-of-view (FOV) imaging. We report <1 sec exposure times and high resolution fluorescence imaging with minimal artifacts.

Optimization and applications of an excitation-scanning hyperspectral imaging system

Currently, the majority of microscopic and endoscopic technologies utilize white light illumination. For a number of applications, hyper-spectral imaging can be shown to have significant improvements over standard white-light imaging techniques. This is true for both microscopy and in vivo imaging. However, hyperspectral imaging methods have suffered from slow application times. Often, minutes are required to gather a full imaging stack. Here we will describe the system and evaluate optimizations and applications of a novel excitation-scanning hyperspectral imaging system. We have developed and are optimizing a novel approach called excitation-scanning hyperspectral imaging that provides an order of magnitude increased signal strength. Optimization of the light path, optical components and illumination sources have allowed us to achieve high speed image acquisition. This high speed allows for potential live video acquisition. This excitation-scanning hyperspectral imaging technology has potential to impact a range of applications. The current system allows triggering of up to 16 wavelengths at less than 1 millisecond per image using digital strobing. Analog intensity control is also provided for a fully customizable excitation profile. A significant advantage of excitation-scanning hyperspectral imaging is can identify multiple targets simultaneously in real time. We are optimizing the system to compare sensitivity and specificity of excitation-scanning hyperspectral imaging with pathology techniques. Finally, we are exploring utilizing this technology to measure cAMP distribution in three dimensions within a cell.

Multispectral Imaging Analysis of Circulating Tumor Cells in Negatively Enriched Peripheral Blood Samples

A variety of biomarkers are present on cells in peripheral blood of patients with a variety of disorders, including solid tumor malignancies. While rare, characterization of these cells for specific protein levels with the advanced technology proposed, will lead to future validation studies of blood samples as "liquid biopsies" for the evaluation of disease status and therapeutic response. While circulating tumor cells (CTCs) have been isolated in the blood samples of patients with solid tumors, the exact role of CTCs as clinically useful predictive markers is still debated. Current commercial technology has significant bias in that a positive selection technology is used that preassumes specific cell surface markers (such as EpCAM) are present on CTCs. However, CTCs with low EpCAM expression have been experimentally demonstrated to be more likely to be missed by this method. In contrast, this application uses a previously developed, technology that performs a purely negative enrichment methodology on peripheral blood, yielding highly enriched blood samples that contain CTCs as well as other, undefined cell types. The focus of this contribution is the use of multispectral imaging of epifluorescent, microscopic images of these enriched cells in order to help develop clinically relevant liquid biopsies from peripheral blood samples.

A four-dimensional snapshot hyperspectral video-endoscope for bio-imaging applications

Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only about 50 wavelengths. We have developed a four-dimensional snapshot hyperspectral video-endoscope with a spectral range of 400–1000 nm, which can detect 756 wavelengths for imaging, significantly more than such systems. Capturing the three-dimensional datacube sequentially gives the fourth dimension. All these are achieved through a flexible two-dimensional to one-dimensional fiber bundle. The potential of this custom designed and fabricated compact biomedical probe is demonstrated by imaging phantom tissue samples in reflectance and fluorescence imaging modalities. It is envisaged that this novel concept and developed probe will contribute significantly towards diagnostic in vivo biomedical imaging in the near future.

Excitation-Scanning Hyperspectral Imaging System for Microscopic and Endoscopic Applications

Current microscopic and endoscopic technologies for cancer screening utilize white-light illumination sources. Hyper-spectral imaging has been shown to improve sensitivity while retaining specificity when compared to white-light imaging in both microscopy and in vivo imaging.1,2 However, hyperspectral imaging methods have historically suffered from slow acquisition times due to the narrow bandwidth of spectral filters. Often minutes are required to gather a full image stack. We have developed a novel approach called excitation-scanning hyperspectral imaging that provides 2-3 orders of magnitude increased signal strength. This reduces acquisition times significantly, allowing for live video acquisition. Here, we describe a preliminary prototype excitation-scanning hyperspectral imaging system that can be coupled with endoscopes or microscopes for hyperspectral imaging of tissues and cells. Our system is comprised of three subsystems: illumination, transmission, and imaging. The illumination subsystem employs light-emitting diode arrays to illuminate at different wavelengths. The transmission subsystem utilizes a unique geometry of optics and a liquid light guide. Software controls allow us to interface with and control the subsystems and components. Digital and analog signals are used to coordinate wavelength intensity, cycling and camera triggering. Testing of the system shows it can cycle 16 wavelengths at as fast as 1 ms per cycle. Additionally, more than 18% of the light transmits through the system. Our setup should allow for hyperspectral imaging of tissue and cells in real time.

Biopsy Diagnosis of Oral Carcinoma by the Combination of Morphological and Spectral Methods Based on Embedded Relay Lens Microscopic Hyperspectral Imaging System

Cytopathological examination through biopsy is very important for carcinoma detection. The embedded relay lens microscopic hyperspectral imaging system (ERL-MHIS) provides a morphological image of a biopsy sample and the spectrum of each pixel in the image simultaneously. Based on the ERL-MHIS, this work develops morphological and spectral methods to diagnose oral carcinoma biopsy. In morphological discrimination, the fractal dimension method is applied to differentiate between normal and abnormal tissues. In spectral identification, normal and cancerous cells are distinguished using five methods. However, the spectra of normal and cancerous cells vary with patient. The diagnostic performances of the five methods are thus not ideal. Hence, the proposed cocktail approach is used to determine the effectiveness of the spectral methods in correlating with the sampling conditions. And then we use a combination of effective spectral methods according to the sample conditions for diagnosing a sample. A total of 68 biopsies from 34 patients are analyzed using the ERL-MHIS. The results demonstrate a sensitivity of 90 ± 4.53 % and a specificity of 87.8 ± 5.21 %. Furthermore, in our survey, this system is the first time utilized to study oral carcinoma biopsies.

Multi-physics simulation and fabrication of a compact 128 × 128 micro-electro-mechanical system Fabry-Perot cavity tunable filter array for infrared hyperspectral imager

This paper demonstrates the design and fabrication of a 128×128 micro-electro-mechanical systems Fabry-Perot (F-P) cavity filter array, which can be applied for the hyperspectral imager. To obtain better mechanical performance of the filters, F-P cavity supporting structures are analyzed by multi-physics finite element modeling. The simulation results indicate that Z-arm is the key component of the structure. The F-P cavity array with Z-arm structures was also fabricated. The experimental results show excellent parallelism of the bridge deck, which agree with the simulation results. A conclusion is drawn that Z-arm supporting structures are important to hyperspectral imaging system, which can achieve a large tuning range and high fill factor compared to straight arm structures. The filter arrays have the potential to replace the traditional dispersive element.

Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions

BACKGROUND

The detection and characterization of resistance reactions of crop plants against fungal pathogens are essential to select resistant genotypes. In breeding practice phenotyping of plant genotypes is realized by time consuming and expensive visual rating. In this context hyperspectral imaging (HSI) is a promising non-invasive sensor technique in order to accelerate and to automate classical phenotyping methods. A hyperspectral microscope was established to determine spectral changes on the leaf and cellular level of barley (Hordeum vulgare) during resistance reactions against powdery mildew (Blumeria graminis f.sp. hordei, isolate K1). Experiments were conducted with near isogenic barley lines of cv. Ingrid, including the susceptible wild type (WT), mildew locus a 12 (Mla12 based resistance) and the resistant mildew locus o 3 (mlo3 based resistance), respectively. The reflection of inoculated and non-inoculated leaves was recorded daily with a hyperspectral linescanner in the visual (400 - 700 nm) and near infrared (700 - 1000 nm) range 3 to 14 days after inoculation.

RESULTS

Data analysis showed no significant differences in spectral signatures between non-inoculated genotypes. Barley leaves of the near-isogenic genotypes, inoculated with B. graminis f.sp. hordei differed in the spectral reflectance over time, respectively. The susceptible genotypes (WT, Mla12) showed an increase in reflectance in the visible range according to symptom development. However, the spectral signature of the resistant mlo-genotype did not show significant changes over the experimental period. In addition, a recent data driven approach for automated discovery of disease specific signatures, which is based on a new representation of the data using Simplex Volume Maximization (SiVM) was applied. The automated approach - evaluated in only a fraction of time revealed results similar to the time and labor intensive manually assessed hyperspectral signatures. The new representation determined by SiVM was also used to generate intuitive and easy to interpretable summaries, e.g. fingerprints or traces of hyperspectral dynamics of the different genotypes.

CONCLUSION

With this HSI based and data driven phenotyping approach an evaluation of host-pathogen interactions over time and a discrimination of barley genotypes differing in susceptibility to powdery mildew is possible.

Protein pathway analysis in Clinical Proteomics using protein microarrays

Molecular diagnostics research within the field of cancer is increasingly focused on detecting low-abundance protein endpoints that can be used to define a patient's disease more completely. Protein microarrays represent an important Clinical Proteomics tool for directly measuring protein endpoints in samples extracted from patient tissues. By combining laser capture microdissection, arraying devices, validated isoform-specific antibodies and advanced reporter technology tools, Clinical Proteomics laboratories are currently generating molecular portraits of cancer cells harvested from patient biopsies.:

Use of colloidal quantum dots as a digitally switched swept light source for gold nanoparticle based hyperspectral microscopy

We propose a method to utilize colloidal quantum dots (QDs) as a swept light source for hyperspectral microscopy. The use of QD allows for uniform multicolor emission which covers visible-NIR wavelengths. We used 8 colors of CdSe/ZnS and CdTe/ZnS colloidal quantum dots with the peak emission wavelengths from 520 nm to 800 nm. The QDs are packed in a compact enclosure, composing a low-cost, solid-state swept light source that can be easily used in most microscopes. Multicolor emission from the QDs is simply controlled by digitally switching excitation UVLEDs, eliminating the use of mechanically-driven gratings or filters. We used gold nanoparticles as optical markers for hyperspectral microscopy. Due to the effect of localized surface plasmon resonance, gold nanoparticles demonstrate size and shape-dependent absorption spectra. Employed in a standard microscope, the QD light source enabled multispectral absorption imaging of macrophage cells labeled with gold nanorods and nanospheres.

Non-de-scanned parallel recording two-photon hyperspectral microscopy with high spectral and spatial resolution

A two-photon hyperspectral microscope with non-de-scanned geometry and parallel recording scheme was constructed which had a low dwell time, high spectral resolution, and high spatial resolution. The dwell time was effectively decreased to reduce photodamage through parallel recording, while the non-de-scanned geometry led to a considerable reduction in the signal loss and spectrum distortion. Two experiments were performed to show that this system can solve crosstalk issues and spectrally resolve the intrinsic fluorophores in optically-thick tissues without staining and sectioning.

Hyperspectral enhanced dark field microscopy for imaging blood cells

In this work, a novel methodology based on hyperspectral imagery with enhanced Darkfield microscopy for probing and characterizing changes in blood cell components was tested. Two main categories of blood cells were analyzed, red and white blood cells. Unique spectral signatures of ordinary and most common deformed morphologies of red blood cells were identified. Moreover, examination of white blood cells allowed to characterize and differentiate active from inactive cells. The findings indicate the ability of this technique to detect changes in light scattering property of blood cells due to their morphological properties Since pathological states can alterate the discocyte shape, this preliminary, but promising application of the hyperspectral analysis to blood cells can be useful to evaluate significant correlations of blood cell spectral features in healthy and pathological conditions. The combination of the quali- and quantitative spectral signatures of hyperspectral imaging microscopy with the information of the subject health conditions may provide a new tool for clinical applications.

Living matter observations with a novel hyperspectral supercontinuum confocal microscope for VIS to near-IR reflectance spectroscopy

A broad range hyper-spectroscopic microscope fed by a supercontinuum laser source and equipped with an almost achromatic optical layout is illustrated with detailed explanations of the design, implementation and data. The real novelty of this instrument, a confocal spectroscopic microscope capable of recording high resolution reflectance data in the VIS-IR spectral range from about 500 nm to 2.5 μm wavelengths, is the possibility of acquiring spectral data at every physical point as defined by lateral coordinates, X and Y, as well as at a depth coordinate, Z, as obtained by the confocal optical sectioning advantage. With this apparatus we collect each single scanning point as a whole spectrum by combining two linear spectral detector arrays, one CCD for the visible range, and one InGaAs infrared array, simultaneously available at the sensor output channel of the home made instrument. This microscope has been developed for biomedical analysis of human skin and other similar applications. Results are shown illustrating the technical performances of the instrument and the capability in extracting information about the composition and the structure of different parts or compartments in biological samples as well as in solid statematter. A complete spectroscopic fingerprinting of samples at microscopic level is shown possible by using statistical analysis on raw data or analytical reflectance models based on Abelés matrix transfer methods.

Detecting the dormant: a review of recent advances in molecular techniques for assessing the viability of bacterial endospores

Due to their contribution to gastrointestinal and pulmonary disease, their ability to produce various deadly exotoxins, and their resistance to extreme temperature, pressure, radiation, and common chemical disinfecting agents, bacterial endospores of the Firmicutes phylum are a major concern for public and environmental health. In addition, the hardy and dormant nature of endospores renders them a particularly significant threat to the integrity of robotic extraterrestrial life-detection investigations. To prevent the contamination of critical surfaces with seemingly ubiquitous bacterial endospores, clean rooms maintained at exceedingly stringent cleanliness levels (i.e., fewer than 100,000 airborne particles per ft(3)) are used for surgical procedures, pharmaceutical processing and packaging, and fabrication and assembly of medical devices and spacecraft components. However, numerous spore-forming bacterial species have been reported to withstand typical clean room bioreduction strategies (e.g., UV lights, maintained humidity, paucity of available nutrients), which highlights the need for rapid and reliable molecular methods for detecting, enumerating, and monitoring the incidence of viable endospores. Robust means of evaluating and tracking spore burden not only provide much needed information pertaining to endospore ecophysiology in different environmental niches but also empower decontamination and bioreduction strategies aimed at sustaining the reliability and integrity of clean room environments. An overview of recent molecular advances in detecting and enumerating viable endospores, as well as the expanding phylogenetic diversity of pathogenic and clean room-associated spore-forming bacteria, ensues.